{"id":"W2295060673","doi":"10.1186/s40064-016-1906-1","title":"Local shape feature fusion for improved matching, pose estimation and 3D object recognition","year":2016,"lang":"en","type":"article","venue":"SpringerPlus","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; University of Washington; Università di Bologna; Strategiske Forskningsråd","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Benchmark (surveying); Matching (statistics); Feature (linguistics); Cognitive neuroscience of visual object recognition; Generalization; Object (grammar); Dimension (graph theory); Feature matching; Overhead (engineering); Feature extraction; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002010423,0.0009769584,0.001461216,0.002407598,0.0004116197,0.001577457,0.001937771,0.001539883,0.00306895],"category_scores_gemma":[0.006333608,0.000486092,0.001338978,0.003846794,0.0009275582,0.003080422,0.002805321,0.001051232,0.001923005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008922687,"about_ca_system_score_gemma":0.0008026693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002272446,"about_ca_topic_score_gemma":0.00229774,"domain_scores_codex":[0.9979183,0.0003417379,0.0001133834,0.0004761519,0.000976893,0.0001734744],"domain_scores_gemma":[0.9980717,0.0004058744,0.0002185913,0.0008993715,0.0003407672,0.00006377931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004031065,0.0001843559,0.002673761,0.0002913214,0.0002052598,0.0001839359,0.0001390197,0.1295843,0.100093,0.01014,0.005570854,0.7505311],"study_design_scores_gemma":[0.00002499304,0.0002199263,0.003384413,0.00003255258,0.00007363419,0.0004481422,0.00006805135,0.8931032,0.08082751,0.01580228,0.005948209,0.00006714572],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01900235,0.0003763273,0.9770144,0.0001124518,0.00005088832,0.00004381516,0.0002544564,0.002349522,0.0007958272],"genre_scores_gemma":[0.4099534,0.0004046222,0.5857379,0.0002284506,0.0000822661,0.0001114254,0.001596871,0.0003389607,0.001546261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00306895,"threshold_uncertainty_score":0.01063222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008350920668422324,"score_gpt":0.2102548894618905,"score_spread":0.2019039687934682,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}